In the field of medical imaging, imaging by X-ray fluoroscopy allows a stream of images of a patient to be supplied to a doctor during surgical interventions referred to as ‘minimally invasive’. The use of these ‘minimally invasive’ operations is becoming more widespread since they cause a lower level of post-operative pain for the patients and because they reduce the risks of infection. These operations require the ability to track medical objects within the arterial system of the patient, for example with the aid of sequences of images acquired by X-rays. Such an intervention can be long and the dose of radiation to which the patient is subjected can have detrimental effects on the health of the patients being exposed. For the health of the medical personnel as well as that of the patient, it is therefore necessary to reduce as much as possible the dose of X-rays received. However, a decrease in the dose of X-rays leads to an increase in the noise and a reduction in the contrast on the images. It is therefore important, if it is desired to be able to reduce the X-ray dosage per image, to have denoising or noise-reduction algorithms available in order to display images of good quality.
Various methods have been developed in the prior art for reducing the noise in images. These methods make use, for example, of wavelet transforms. The major drawback of wavelet transforms is their difficulty in correctly taking the contours of the images into account. The number of orientations that a wavelet can take depends on the type used. In any case, the basic functions do not exhibit anisotropy, which does not endow them with optimal properties for the representation of the curves and, at the most, a real wavelet can take 6 different orientations. This number is insufficient for representing in an optimal manner the diversity of the orientations contained in a natural image.
In the document entitled: “A device enhancing and denoising algorithm for x-ray cardiac fluoroscopy”, published in Pattern Recognition, 2008, ICPR, 2008, 19th International Conference on, pp. 1-4, IEEE, 2008, Bismuth et al. use the wavelet transform in order to obtain a denoised background, but this has a tendency to interfere with the contours.
In the document entitled “Adaptive spatio-temporal denoising of fluoroscopic x-ray sequences” published in Biomedical Signal Processing and control, vol. 7, n°2, pp 173-179, 2012, Tomic et al. promote a spatio-temporal approach, since they re-index their 2D+time signal into a 1D signal. Thus, after the application of a wavelet transform preserving the contours, they obtain a signal denoised in both the space and time domains. However, because of the re-indexation, the method does not allow significant movements, such as heart movements, to be taken into account which then require a spatial repositioning.
Other methods have concentrated only on the time-behavior aspect, in order to take advantage of the redundancy of the images from the sequence and to avoid a spatial coloring of the noise. One method described in the document by Auvray et al., “Joint motion estimation and layer segmentation in transparent image sequences-Application to transparent-motion-compensated noise reduction in x-ray image sequences”, 2008, provides a time-domain filtering while compensating for the movements. It is possible to make the approximation that one pixel is associated with a single movement vector. This simplifies the method but introduces errors. Since the flouroscopic images are multiplicative, the anatomical layers are superposed. However, they are subject to different movements and it is probable that several objects following different paths appear on the same pixel. The idea of Auvray et al. is thus to separate the transparent layers and to evaluate the movement vectors on each of these layers. This method has the drawback of having to evaluate the number of transparent layers in each part of the image, which is extremely difficult to obtain.
FIG. 1 shows schematically a method known from the prior art which implements the spatial aspect of the denoising by the curvelets method. The image undergoes a step for 2D spatial transform, then the correlation properties of the coefficients are verified. If these properties are not complied with, the amplitude of the coefficients in question is modified prior to applying the thresholding function. The method uses, for example, three thresholds bounding four regions in the thresholding function. These regions are used for verifying the coherence between the coefficients and to thus detect significant changes which might be due to noise. The algorithm having been currently developed allows the contours to be preserved during the denoising.
The spatial processing described allows the number of artifacts present in the denoised image to be reduced, but it does not allow it to be totally eliminated. In particular, the lower the dose, the more the number of artifacts increases. The low spatial redundancy and the time constraints do not allow the performance characteristics of this processing to be improved. It is nevertheless possible to use it with a time-domain filtering which averages the values of a pixel of the image over time. This filter is applied prior to the spatial filtering, and the combined use of the two allows denoised images to be obtained without artifacts. However, in order to preserve the objects in motion, the denoising capacities of the time-domain filter are reduced. Its use therefore limits the possibilities with regard to the reduction in X-ray dose.
The publication entitled “Curvelet transform-based technique for tracking of moving objects” by S. Nigam and A. Khare, Computer Vision, IET, vol. 6, no. 3, pp 231-251, 2012 describes a method for tracking objects in curvelets. It uses the conservation of the total energy (inter- and intra-scale) in order to update the position of the object being tracked. The results have been published for non-noisy videos and show better tracking results than the spatial methods or those for tracking in wavelets.